SoroushVahidi/scidocs huggingface dataset:
Development of a Danish Language Version of the Manchester Foot Pain and Disability Index: Reproducibility and Construct Validity Testing
(2013)

Introduction. The Manchester Foot Pain and Disability Index (MFPDI) is a 19-item questionnaire for the assessment of disability caused by foot pain. The aim was to develop a Danish language version of the MFPDI (MFPDI-DK) and evaluate its reproducibility and construct validity. Methods. A Danish version was created, following a forward-backward translation procedure. A sample of 84 adult patients with foot pain was recruited. Participants completed two copies of the MFPDI-DK within a 24- to 48-hour interval, along with the Medical Outcomes Study Short Form 36 (SF-36), and a pain Visual Analog Scale (VAS). Reproducibility was assessed using the intraclass correlation coefficient (ICC) and 95% limits of agreement (Bland-Altman plot). Construct validity was evaluated with Pearson's Rho, using a priori hypothesized correlations with SF-36 subscales and VASmean. Results. The MFPDI-DK showed very good reliability with an ICC of 0.92 (0.88-0.95). The 95% limits of agreement ranged from -6.03 to 6.03 points. Construct validity was supported by moderate to very strong correlations with the SF-36 physical subscales and VASmean. Conclusion. The MFPDI-DK appears to be a valid and reproducible instrument in evaluating foot-pain-related disability in Danish adult patients in cross-sectional samples. Further research is needed to test the responsiveness of the MFPDI-DK.
A retrospective study of SPECT/CT scans using SUV measurement of the normal pelvis with Tc-99m methylene diphosphonate.
(2018)

OBJECTIVE
To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner.


MATERIAL AND METHODS
This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed.


RESULTS
The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values.


CONCLUSIONS
Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.
Challenges in automotive software engineering
(2006)

The amount of software in cars grows exponentially. Driving forces of this development are cheaper and more powerful hardware and the demand for innovations by new functions. The rapid increase of software and software based functionality brings various challenges (see [21], [23], [25], [26]) for the automotive industries, for their organization, key competencies, processes, methods, tools, models, product structures, division of work, logistics, maintenance, and long term strategies. From a software engineering perspective, the automotive industry is an ideal and fascinating application domain for advanced techniques. Although the automotive industry may adopt general results and solutions from the software engineering body of knowledge gained in other domains, the specific constraints and domain specific requirements in the automotive industry ask for individual solutions and bring various challenges for automotive software engineering. In cars we find literally all interesting problems and challenging issues of software and systems engineering.
Proofs of Work for Blockchain Protocols
(2017)

One of the most impactful applications of proofs of work (POW) currently is in the design of blockchain protocols such as Bitcoin. Yet, despite the wide recognition of POWs as the fundamental cryptographic tool in this context, there is no known cryptographic formulation that implies the security of the Bitcoin blockchain protocol. Indeed, all previous works formally arguing the security of the Bitcoin protocol relied on direct proofs in the random oracle model, thus circumventing the di culty of isolating the required properties of the core POW primitive. In this work we ll this gap by providing a formulation of the POW primitive that implies the security of the Bitcoin blockchain protocol in the standard model. Our primitive entails a number of properties that parallel an e cient non-interactive proof system: completeness and fast veri cation, security against malicious provers (termed hardness against tampering and chosen message attacks ) and security for honest provers (termed uniquely successful under chosen key and message attacks ). Interestingly, our formulation is incomparable with previous formulations of POWs that applied the primitive to contexts other than the blockchain. Our result paves the way for proving the security of blockchain protocols in the standard model assuming our primitive can be realized from computational assumptions.
Some Theoretical Considerations in Off-the-Shelf Text Analysis Software
(2015)

This paper is concerned with theoretical considerations of commercial content analysis software, namely Linguistic Inquiry and Word Count (LIWC), developed by social psychologists at the University of Texas. LIWC is widely cited and forms the basis of many research papers from a range of disciplines. Here, LIWC is taken as an example of a context-independent, word-counting approach to text analysis, and the strengths and potential pitfalls of such a methodology are discussed. It is shown that text analysis software is constrained not only by its functions, but also by its underlying theoretical assumptions. The paper offers recommendations for good practice in software commercialisation and application, stressing the importance of transparency and acknowledgement of biases.
Mutation Spectrum and Phenotypic Features in Noonan Syndrome with PTPN11 Mutations: Definition of Two Novel Mutations.
(2016)

OBJECTIVES
To evaluate the spectrum of PTPN11 gene mutations in Noonan syndrome patients and to study the genotype-phenotype associations.


METHODS
In this study, twenty Noonan syndrome patients with PTPN11 mutations were included. The patients underwent a detailed clinical and physical evaluation. To identify inherited cases, parents of all mutation positive patients were analyzed.


RESULTS
Thirteen different PTPN11 mutations, two of them being novel, were detected in the study group. These mutations included eleven missense mutations: p.G60A, p.D61N, p.Y62D, p.Y63C, p.E69Q, p.Q79R, p.Y279C,p.N308D, p.N308S, p.M504V, p.Q510R and two novel missense mutations: p.I56V and p.I282M. The frequency of cardiac abnormalities and short stature were found to be 80 % and 80 %, respectively. Mental retardation was not observed in patients having exon 8 mutations. No significant correlations were detected between other phenotypic features and genotypes.


CONCLUSIONS
By identifying genotype-phenotype correlations, this study provides information on phenotypes observed in NS patients with different PTPN11 mutations.
Recurrent Neural Network Regularization
(2014)

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, and machine translation.
Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription
(2012)

We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.
Efficient Genome-Wide, Privacy-Preserving Similar Patient Query based on Private Edit Distance
(2015)

Edit distance has been proven to be an important and frequently-used metric in many human genomic research, with Similar Patient Query (SPQ) being a particularly promising and attractive example. However, due to the widespread privacy concerns on revealing personal genomic data, the scope and scale of many novel use of genome edit distance are substantially limited. While the problem of private genomic edit distance has been studied by the research community for over a decade [6], the state-of-the-art solution [31] is far from even close to be applicable to real genome sequences. In this paper, we propose several private edit distance protocols that feature unprecedentedly high efficiency and precision. Our construction is a combination of a novel genomic edit distance ap- proximation algorithm and new construction of private set difference size protocols. With the private edit distance based secure SPQ primitive, we propose GENSETS, a genome-wide, privacy- preserving similar patient query system. It is able to support search- ing large-scale, distributed genome databases across the nation. We have implemented a prototype of GENSETS. The experimental results show that, with 100 Mbps network connection, it would take GENSETS less than 200 minutes to search through 1 million breast cancer patients (distributed nation-wide in 250 hospitals, each having 4000 patients), based on edit distances between their genomes of lengths about 75 million nucleotides each.
IBM Mega Traffic Simulator
(2012)

IBM Mega Traffic Simulator (Megaffic) is an agent-based simulator of traffic flows with two unique features. First, Megaffic can build its model of simulation by directly estimating some of the parameters of the model from probe-car data. This capability is in contrast to existing agent-based simulators of traffic flows, where the values of their parameters are calibrated with iterative simulation. Second, Megaffic can run on massively parallel computers and simulate the microscopic traffic flows in the scale of an arbitrary city or even the whole Japan. This manuscript gives an overview of the design and capability of Megaffic. ∗5-6-52 Toyosu, Koto-ku, Tokyo 135-8511, Japan
Multilinear Sparse Principal Component Analysis
(2014)

In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.
CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System
(2004)

CorMet is a corpus-based system for discovering metaphorical mappings between concepts. It does this by finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora. Metaphors transfer structure from a source domain to a target domain, making some concepts in the target domain metaphorically equivalent to concepts in the source domain. The verbs that select for a concept in the source domain tend to select for its metaphorical equivalent in the target domain. This regularity, detectable with a shallow linguistic analysis, is used to find the metaphorical interconcept mappings, which can then be used to infer the existence of higher-level conventional metaphors. Most other computational metaphor systems use small, hand-coded semantic knowledge bases and work on a few examples. Although Cor Met's only knowledge base is Word Net (Fellbaum 1998) it can find the mappings constituting many conventional metaphors and in some cases recognize sentences instantiating those mappings. CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991).
Metagenes and molecular pattern discovery using matrix factorization.
(2004)

We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.
Discriminative Training of Deep Fully Connected Continuous CRFs With Task-Specific Loss
(2017)

Recent works on deep conditional random fields (CRFs) have set new records on many vision tasks involving structured predictions. Here, we propose a fully connected deep continuous CRF model with task-specific losses for both discrete and continuous labeling problems. We exemplify the usefulness of the proposed model on multi-class semantic labeling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNNs), which are jointly learned in an end-to-end fashion. The proposed method possesses the main advantage of continuously valued CRFs, which is a closed-form solution for the maximum a posteriori (MAP) inference. To better take into account the quality of the predicted estimates during the cause of learning, instead of using the commonly employed maximum likelihood CRF parameter learning protocol, we propose task-specific loss functions for learning the CRF parameters. It enables direct optimization of the quality of the MAP estimates during the learning process. Specifically, we optimize the multi-class classification loss for the semantic labeling task and the Tukey’s biweight loss for the robust depth estimation problem. Experimental results on the semantic labeling and robust depth estimation tasks demonstrate that the proposed method compare favorably against both baseline and state-of-the-art methods. In particular, we show that although the proposed deep CRF model is continuously valued, with the equipment of task-specific loss, it achieves impressive results even on discrete labeling tasks.
C V ] 2 1 N ov 2 01 7 Emotion Classification with Data Augmentation Using Generative Adversarial Networks
(2017)

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework with a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN’s performance. Empirical results show that we can obtain 5%∼10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.
AMOEBA: HIERARCHICAL CLUSTERING BASED ON SPATIAL PROXIMITY USING DELAUNATY DIAGRAM
(2000)

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. We propose an exploratory method that reveals a robust clustering hierarchy. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require any prior knowledge about the data set, nor does it require parameters from the user. Multi-level clusters are successfully discovered by this new method in only O(nlogn) time, where n is the size of the data set. The efficiency of our method allows us to construct and display a new type of tree graph that facilitates understanding of the complex hierarchy of clusters. We show that clustering methods that adopt a raster-like or vector-like representation of proximity are not appropriate for spatial clustering. We illustrate the robustness of our method with an experimental evaluation with synthetic data sets as well as real data sets.
Service-oriented middleware for the Future Internet: state of the art and research directions
(2011)

Service-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last 10 years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today’s service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore, investigating service description, discovery, access, and composition in the Future Internet of services.
Analysis of the Co-purchase Network of Products to Predict Amazon Sales-Rank
(2017)

Amazon sales-rank gives a relative estimate of a product item’s popularity among other items in the same category. An early prediction of the Amazon sales-rank of a product would imply an early guess of its sales-popularity relative to the other products on Amazon, which is one of the largest e-commerce hub across the globe. Traditional methods suggest use of product review related features, e.g., volume of reviews, text content of the reviews etc. for the purpose of prediction. In contrast, we propose in this paper for the first time a network-assisted approach to construct suitable features for prediction. In particular, we build a co-purchase network treating the individual products as nodes, with edges in between if two products are bought with one another. The way a product is positioned in this network (e.g., its centrality, clustering coefficient etc.) turns out to be a strong indicator of its sales-rank. This network-assisted approach has two distinct advantages over the traditional baseline method based on review analysis – (i) it works even if the product has no reviews (relevant especially in the early stages of the product launch) and (ii) it is notably more discriminative in classifying a popular (i.e., low sales-rank) product from an unpopular (i.e., high sales-rank) one. Based on this observation, we build a supervised model to early classify a popular product from an unpopular one. We report our results on two different product categories (CDs and cell phones) and obtain remarkably better classification accuracy compared to the baseline scheme. When the top 100 (700) products based on sales-rank are labelled as popular and the bottom 100 (700) are labelled as unpopular, the classification accuracy of our method is 89.85% (82.1%) for CDs and 84.11% (84.8%) for cell phones compared to 46.37% (68.75%) and 83.17% (71.95%) respectively from the baseline method.
IMPROVING WEB SERVICE CLUSTERING THROUGH POST FILTERING TO BOOTSTRAP THE SERVICE DISCOVERY
(2014)

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine-grained information. Thus, the approaches couldn’t identify the correct clusters for some services and placed them in wrong clusters. As a result of this, cluster performance is reduced. This paper proposes post-filtering approach to increase the performance of clusters by rearranging services incorrectly clustered. Our approach uses context aware similarity method that learns domain context by machine learning to produce models of context for terms retrieved from the Web in the filtering process to calculate the service similarity. We applied post filtering approach to hybrid term similarity based clustering approach that we proposed in our previous work. Experimental results show that our post-filtering approach works efficiently.
A difference in hypothalamic structure between heterosexual and homosexual men.
(1991)

The anterior hypothalamus of the brain participates in the regulation of male-typical sexual behavior. The volumes of four cell groups in this region [interstitial nuclei of the anterior hypothalamus (INAH) 1, 2, 3, and 4] were measured in postmortem tissue from three subject groups: women, men who were presumed to be heterosexual, and homosexual men. No differences were found between the groups in the volumes of INAH 1, 2, or 4. As has been reported previously, INAH 3 was more than twice as large in the heterosexual men as in the women. It was also, however, more than twice as large in the heterosexual men as in the homosexual men. This finding indicates that INAH is dimorphic with sexual orientation, at least in men, and suggests that sexual orientation has a biological substrate.